Datasets and evaluation.
The models are evaluated and compared on three benchmark datasets, i.e., CUB-200-2011 (CUB) [1]}, Cars-196 (Cars) [2]}, and Stanford Online Product (SOP) [3]};
for their train-test splits, we directly follow the standard protocol presented in [4]}.
Performance on these datasets is evaluated by Recall@\(k\) , the fraction of queries that have at least one relevant sample in their \(k\) -nearest neighbors on a learned embedding space.
